Fortune

A.I. SPECIAL REPORT
Posner: “You are going to mitigate risks of bias
if you have more diversity.”
older people, or overweight people, you could add weight to photos
of such individuals to make up for the shortage in your data set.
Other engineers are focusing further “upstream”—making sure
that the underlying data used to train algorithms is inclusive and
free of bias, before it’s even deployed. In image recognition, for example,
the millions of images used to train deep-learning systems
need to be examined and labeled before they are fed to computers.
Radha Basu, the CEO of data-training startup iMerit, whose
clients include Getty Images and eBay, explains that the company’s
staf of over 1,400 worldwide is trained to label photos on behalf of
its customers in ways that can mitigate bias.
Basu declined to discuss how that might play out when labeling
people, but she ofered other analogies. iMerit staf in India may
consider a curry dish to be “mild,” while the company’s staf in New
Orleans may describe the same meal as “spicy.” iMerit would make
sure both terms appear in the label for a photo of that dish, because
to label it as only one or the other would be to build an inaccuracy
into the data. Assembling a data set about weddings, iMerit would
include traditional Western white-dress-and-layer-cake images—but
also shots from elaborate, more colorful weddings in India or Africa.
iMerit’s staf stands out in a diferent way, Basu notes: It includes
people with Ph.D.s, but also less-educated people who struggled
with poverty, and 53% of the staf are women. The mix ensures that
as many viewpoints as possible are involved in the data labeling
process. “Good ethics does not just involve privacy and security,”
Basu says. “It’s about bias, it’s about, Are we missing a viewpoint?”
Tracking down that viewpoint is becoming part of more tech companies’
strategic agendas. Google, for example, announced in June
that it would open an A.I. research center later this year in Accra,
Ghana. “A.I. has great potential to positively impact the world, and
more so if the world is well represented in the development of new
A.I. technologies,” two Google engineers wrote in a blog post.
A.I. insiders also believe they can fight bias by making their
workforces in the U.S. more diverse—always a hurdle for Big Tech.
Fei-Fei Li, the Google executive, recently cofounded the nonprofit
AI4ALL to promote A.I. technologies and education among girls
and women and in minority communities. The group’s activities
include a summer program in which campers visit top university
A.I. departments to develop relationships with mentors and role
models. The bottom line, says AI4ALL executive director Tess
> ralf herbrich
director of a.i., amazon
“age, gender, race,
nationality—they
are all dimensions
that you have to test
the sampling biases
for over time.”
EARS BEFORE this more diverse
generation of A.I. researchers
Y reaches the job market, however,
big tech companies will have
further imbued their products
with deep-learning capabilities. And even as
top researchers increasingly recognize the
technology’s flaws—and acknowledge that
they can’t predict how those flaws will play
out—they argue that the potential benefits,
social and financial, justify moving forward.
“I think there’s a natural optimism about
what technology can do,” says Candela, the
Facebook executive. Almost any digital tech
can be abused, he says, but adds, “I wouldn’t
want to go back to the technology state we
had in the 1950s and say, ‘No, let’s not deploy
these things because they can be used wrong.’ ”
Horvitz, the Microsoft research chief, says
he’s confident that groups like his Aether
team will help companies solve potential bias
problems before they cause trouble in public.
“I don’t think anybody’s rushing to ship things
that aren’t ready to be used,” he says. If anything,
he adds, he’s more concerned about “the
ethical implications of not doing something.”
He invokes the possibility that A.I. could
reduce preventable medical error in hospitals.
“You’re telling me you’d be worried that my
system [showed] a little bit of bias once in a
while?” Horvitz asks. “What are the ethics of
not doing X when you could’ve solved a problem
with X and saved many, many lives?”
The watchdogs’ response boils down to:
Show us your work. More transparency and
openness about the data that goes into A.I.’s
black-box systems will help researchers spot
bias faster and solve problems more quickly.
When an opaque algorithm could determine
whether a person can get insurance, or whether
that person goes to prison, says Buolamwini,
the MIT researcher, “it’s really important that
we are testing these systems rigorously, that
there are some levels of transparency.”
Indeed, it’s a sign of progress that few
people still buy the idea that A.I. will be
infallible. In the web’s early days, notes Tim
Hwang, a former Google public policy executive
for A.I. who now directs the Harvard-
MIT Ethics and Governance of Artificial
Intelligence initiative, technology companies
could say they are “just a platform that represents
the data.” Today, “society is no longer
willing to accept that.”
www.t.me/velarch_official
HERBRICH: TOBIAS KOCH— COURTESY OF AMAZON
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FORTUNE.COM // JULY.1.18